A Modeling Comparison of Groundwater Potential Mapping in a Mountain Bedrock Aquifer: QUEST, GARP, and RF Models

In some arid regions, groundwater is the only source of water for human needs, so understanding groundwater potential is essential to ensure its sustainable use. In this study, three machine learning models (Genetic Algorithm for Rule-Set Production (GARP), Quick Unbiased Efficient Statistical Tree (QUEST), and Random Forest (RF)) were applied and verified for spatial prediction of groundwater in a mountain bedrock aquifer in Piranshahr Watershed, Iran. A spring location dataset consisting of 141 springs was prepared by field surveys, and from this three different sample datasets (S1–S3) were randomly generated (70% for training and 30% for validation). A total of 10 groundwater conditioning factors were prepared for modeling, namely slope percent, relative slope position (RSP), plan curvature, altitude, drainage density, slope aspect, topographic wetness index (TWI), terrain ruggedness index (TRI), land use, and lithology. The area under the receiver operating characteristic curve (AUC) and true skill statistic (TSS) were used to evaluate the accuracy of models. The results indicated that all models had excellent goodness-of-fit and predictive performance, but that RF (AUCmean = 0.995, TSSmean = 0.89) and GARP (AUCmean = 0.957, TSSmean = 0.82) outperformed QUEST (AUCmean = 0.949, TSSmean = 0.74). In robustness analysis, RF was slightly more sensitive than GARP and QUEST, making it necessary to consider several random partitioning options for preparing training and validation groups. The outcomes of this study can be useful in sustainable management of groundwater resources in the study region.

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Harris Hawks Optimization: A Novel Swarm Intelligence Technique for Spatial Assessment of Landslide Susceptibility

Sensors ◽

10.3390/s19163590 ◽

2019 ◽

Vol 19 (16) ◽

pp. 3590 ◽

Cited By ~ 38

Author(s):

Bui ◽

Moayedi ◽

Kalantar ◽

Osouli ◽

Gör ◽

...

Keyword(s):

Landslide Susceptibility ◽

Characteristic Curve ◽

Absolute Error ◽

Slope Aspect ◽

Stream Power ◽

Topographic Wetness Index ◽

Conditioning Factors ◽

Performance Error ◽

Stream Power Index ◽

Artificial Neural Network Ann

In this research, the novel metaheuristic algorithm Harris hawks optimization (HHO) is applied to landslide susceptibility analysis in Western Iran. To this end, the HHO is synthesized with an artificial neural network (ANN) to optimize its performance. A spatial database comprising 208 historical landslides, as well as 14 landslide conditioning factors—elevation, slope aspect, plan curvature, profile curvature, soil type, lithology, distance to the river, distance to the road, distance to the fault, land cover, slope degree, stream power index (SPI), topographic wetness index (TWI), and rainfall—is prepared to develop the ANN and HHO–ANN predictive tools. Mean square error and mean absolute error criteria are defined to measure the performance error of the models, and area under the receiving operating characteristic curve (AUROC) is used to evaluate the accuracy of the generated susceptibility maps. The findings showed that the HHO algorithm effectively improved the performance of ANN in both recognizing (AUROCANN = 0.731 and AUROCHHO–ANN = 0.777) and predicting (AUROCANN = 0.720 and AUROCHHO–ANN = 0.773) the landslide pattern.

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Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17144933 ◽

2020 ◽

Vol 17 (14) ◽

pp. 4933 ◽

Cited By ~ 4

Author(s):

Viet-Ha Nhu ◽

Ayub Mohammadi ◽

Himan Shahabi ◽

Baharin Bin Ahmad ◽

Nadhir Al-Ansari ◽

...

Keyword(s):

Landslide Susceptibility ◽

Predictive Value ◽

Vegetation Index ◽

Performance Metrics ◽

Characteristic Curve ◽

Google Earth ◽

Machine Learning Algorithms ◽

Landslide Susceptibility Mapping ◽

Slope Aspect ◽

Topographic Wetness Index

We used AdaBoost (AB), alternating decision tree (ADTree), and their combination as an ensemble model (AB-ADTree) to spatially predict landslides in the Cameron Highlands, Malaysia. The models were trained with a database of 152 landslides compiled using Synthetic Aperture Radar Interferometry, Google Earth images, and field surveys, and 17 conditioning factors (slope, aspect, elevation, distance to road, distance to river, proximity to fault, road density, river density, normalized difference vegetation index, rainfall, land cover, lithology, soil types, curvature, profile curvature, stream power index, and topographic wetness index). We carried out the validation process using the area under the receiver operating characteristic curve (AUC) and several parametric and non-parametric performance metrics, including positive predictive value, negative predictive value, sensitivity, specificity, accuracy, root mean square error, and the Friedman and Wilcoxon sign rank tests. The AB model (AUC = 0.96) performed better than the ensemble AB-ADTree model (AUC = 0.94) and successfully outperformed the ADTree model (AUC = 0.59) in predicting landslide susceptibility. Our findings provide insights into the development of more efficient and accurate landslide predictive models that can be used by decision makers and land-use managers to mitigate landslide hazards.

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Characterizing Groundwater Potential Using GIS-Based Machine Learning Model in Chihe River Basin, China

10.21203/rs.3.rs-1044219/v1 ◽

2021 ◽

Author(s):

Dejian Wang ◽

Jiazhong Qian ◽

Lei Ma ◽

Weidong Zhao ◽

Di Gao ◽

...

Keyword(s):

Machine Learning ◽

Water Resources ◽

River Basin ◽

Environmental Variables ◽

Groundwater Potential ◽

High Potential ◽

Slope Aspect ◽

Learning Models ◽

Topographic Wetness Index ◽

Machine Learning Models

Abstract Mapping of groundwater potential over space, built by synergizing environmental variables and machine learning models, was of great significance for regional water resources management. Taking the Chihe River basin in Anhui province as an example, thirteen influence factors were used to predict the spatial distribution of groundwater, including elevation, slope, aspect, plan curvature, profile curvature, topographic wetness index (TWI), drainage density, distance to rivers, distance to faults, lithology, soil type, land use, and normalized difference vegetation index (NDVI). The potential of groundwater resource in this region was predicted using GIS-based machine learning models, including logistic regression (LR), deep neural networks (DNN), and random forest (RF) model. Then, the accuracy of prediction results was evaluated by calculating the RMSE, MAE and R evaluation index. The results show that there is no collinearity among the 13 environmental impact factors, which can provide corresponding environmental variables for the evaluation of regional groundwater potential. Machine learning models show that groundwater potential is concentrated in moderate to high potential areas. Among them, the moderate to the high potential of this area accounted for 81.14% in the LR model, 90.36% and 87.55% in the DNN model and the RF model, respectively. According to the result of these evaluation indexes, the three models all have high prediction accuracy, among which the LR model performs more prominently. The good prediction capabilities of these machine learning technologies can provide a reliable scientific basis for spatial prediction of groundwater potential and management of water resources.

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Gully Erosion Susceptibility Assessment in the Kondoran Watershed Using Machine Learning Algorithms and the Boruta Feature Selection

Sustainability ◽

10.3390/su131810110 ◽

2021 ◽

Vol 13 (18) ◽

pp. 10110

Author(s):

Hamed Ahmadpour ◽

Ommolbanin Bazrafshan ◽

Elham Rafiei-Sardooi ◽

Hossein Zamani ◽

Thomas Panagopoulos

Keyword(s):

Gully Erosion ◽

Susceptibility Mapping ◽

Management Tool ◽

Slope Aspect ◽

Support Vector ◽

Ensemble Model ◽

Operating Characteristics ◽

Topographic Wetness Index ◽

Study Region ◽

Receiver Operating

Gully erosion susceptibility mapping is an essential land management tool to reduce soil erosion damages. This study investigates gully susceptibility based on multiple diagnostic analysis, support vector machine and random forest algorithms, and also a combination of these models, namely the ensemble model. Thus, a gully susceptibility map in the Kondoran watershed of Iran was generated by applying these models on the occurrence and non-occurrence points (as the target variable) and several predictors (slope, aspect, elevation, topographic wetness index, drainage density, plan curvature, distance to streams, lithology, soil texture and land use). The Boruta algorithm was used to select the most effective variables in modeling gully erosion susceptibility. The area under the receiver operating characteristic curve (AUC), the receiver operating characteristics, and true skill statistics (TSS) were used to assess the model performance. The results indicated that the ensemble model had the best performance (AUC = 0.982, TSS = 0.93) compared to the others. The most effective factors in gully erosion susceptibility mapping of the study region were topological, anthropogenic, and geological. The methodology and variables of this study can be used in other regions to control and mitigate the gully erosion phenomenon by applying biophilic and regenerative techniques at the locations of the most influential factors.

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Groundwater-Potential Mapping Using a Self-Learning Bayesian Network Model: A Comparison among Metaheuristic Algorithms

Water ◽

10.3390/w13050658 ◽

2021 ◽

Vol 13 (5) ◽

pp. 658

Author(s):

Sadegh Karimi-Rizvandi ◽

Hamid Valipoori Goodarzi ◽

Javad Hatami Afkoueieh ◽

Il-Moon Chung ◽

Ozgur Kisi ◽

...

Keyword(s):

Bayesian Network ◽

Groundwater Resources ◽

Slope Angle ◽

Drainage Density ◽

Groundwater Potential ◽

Estimation Accuracy ◽

Slope Aspect ◽

Khuzestan Province ◽

Potential Mapping ◽

Groundwater Potential Mapping

Owing to the reduction of surface-water resources and frequent droughts, the exploitation of groundwater resources has faced critical challenges. For optimal management of these valuable resources, careful studies of groundwater potential status are essential. The main goal of this study was to determine the optimal network structure of a Bayesian network (BayesNet) machine-learning model using three metaheuristic optimization algorithms—a genetic algorithm (GA), a simulated annealing (SA) algorithm, and a Tabu search (TS) algorithm—to prepare groundwater-potential maps. The methodology was applied to the town of Baghmalek in the Khuzestan province of Iran. For modeling, the location of 187 springs in the study area and 13 parameters (altitude, slope angle, slope aspect, plan curvature, profile curvature, topography wetness index (TWI), distance to river, distance to fault, drainage density, rainfall, land use/cover, lithology, and soil) affecting the potential of groundwater were provided. In addition, the statistical method of certainty factor (CF) was utilized to determine the input weight of the hybrid models. The results of the OneR technique showed that the parameters of altitude, lithology, and drainage density were more important for the potential of groundwater compared to the other parameters. The results of groundwater-potential mapping (GPM) employing the receiver operating characteristic (ROC) area under the curve (AUC) showed an estimation accuracy of 0.830, 0.818, 0.810, and 0.792, for the BayesNet-GA, BayesNet-SA, BayesNet-TS, and BayesNet models, respectively. The BayesNet-GA model improved the GPM estimation accuracy of the BayesNet-SA (4.6% and 7.5%) and BayesNet-TS (21.8% and 17.5%) models with respect to the root mean square error (RMSE) and mean absolute error (MAE), respectively. Based on metric indices, the GA provides a higher capability than the SA and TS algorithms for optimizing the BayesNet model in determining the GPM.

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Improvement of Credal Decision Trees Using Ensemble Frameworks for Groundwater Potential Modeling

Sustainability ◽

10.3390/su12072622 ◽

2020 ◽

Vol 12 (7) ◽

pp. 2622 ◽

Cited By ~ 8

Author(s):

Phong Tung Nguyen ◽

Duong Hai Ha ◽

Huu Duy Nguyen ◽

Tran Van Phong ◽

Phan Trong Trinh ◽

...

Keyword(s):

Decision Trees ◽

Flow Direction ◽

Groundwater Resources ◽

Groundwater Potential ◽

Hybrid Models ◽

Topographic Wetness Index ◽

Conditioning Factors ◽

Statistical Measures ◽

The World ◽

Potential Mapping

Groundwater is one of the most important sources of fresh water all over the world, especially in those countries where rainfall is erratic, such as Vietnam. Nowadays, machine learning (ML) models are being used for the assessment of groundwater potential of the region. Credal decision trees (CDT) is one of the ML models which has been used in such studies. In the present study, the performance of the CDT has been improved using various ensemble frameworks such as Bagging, Dagging, Decorate, Multiboost, and Random SubSpace. Based on these methods, five hybrid models, namely BCDT, Dagging-CDT, Decorate-CDT, MBCDT, and RSSCDT, were developed and applied for groundwater potential mapping of DakLak province of Vietnam. Data of 227 groundwater wells of the study area were utilized for the construction and validation of the models. Twelve groundwater potential conditioning factors, namely rainfall, slope, elevation, river density, Sediment Transport Index (STI), curvature, flow direction, aspect, soil, land use, Topographic Wetness Index (TWI), and geology, were considered for the model studies. Various statistical measures, including area under receiver operating characteristic (AUC) curve, were applied to validate and compare the performance of the models. The results show that performance of the hybrid CDT ensemble models MBCDT (AUC = 0.770), BCDT (AUC = 0.731), Dagging-CDT (AUC = 0.763), Decorate-CDT (AUC = 0.750), and RSSCDT (AUC = 0.766) improved significantly in comparison to the single CDT (AUC = 0.722) model. Therefore, these developed hybrid models can be applied for better ground water potential mapping and groundwater resources management of the study area as well as other regions of the world.

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AHP and GIS-based Delineation of Groundwater Potential of Papumpare District of Arunachal Pradesh (India)

10.21203/rs.3.rs-350312/v1 ◽

2021 ◽

Author(s):

Ranji Mahato ◽

Dhoni Bushi ◽

Gibji Nimasow ◽

Oyi Dai Nimasow ◽

Ramesh Chandra Joshi

Keyword(s):

Surface Water ◽

State Government ◽

Groundwater Resources ◽

Drainage Density ◽

Groundwater Potential ◽

Geographical Information ◽

Sensitivity Analyses ◽

Arunachal Pradesh ◽

Topographic Wetness Index ◽

Precise Knowledge

Abstract Water is crucial to human survival. Studies on surface water are well documented but precise knowledge of groundwater resources is difficult. Thus, accurate knowledge of groundwater resources could meet the necessities of water at present and in the long run. The application of the Analytic Hierarchy Process (AHP) and Geographical Information System (GIS) together with multi-criteria parameters has emerged as an efficient technique for delineation of groundwater potential in recent decades. However, no efforts to delineate the groundwater potential have been attempted in the study area to date. Hence, in this study, the groundwater potential of Papumpare district of Arunachal Pradesh was delineated by combining AHP, GIS, and ten thematic layers (geomorphology, geology, slope, lineament density, drainage density, rainfall, distance from the major river, topographic wetness index, soil texture, and land use/land cover). The results show about 64% of the area under poor groundwater potential. Moderate and good groundwater potential is found in 31% and 5% of the area, respectively. Map-removal and single-parameter sensitivity analyses revealed that the groundwater potential map is most sensitive to the annual average rainfall with a mean variation index of 1.05% and a weight of 19.07%. The flood/alluvial plains, Siwalik formations with sediments, and level to gentle slopes receiving high rainfall show good potential, and the dissected hills/valleys, metamorphic rock assemblages, steep slopes with low rainfall reveals poor groundwater potential. The overall accuracy of 81.25% with a Kappa coefficient of 0.72 explains good agreement between the reference data and the map. The estimated area under good groundwater potential appears too little concerning the increasing population and urbanization. Therefore, the state government in general and the water resources and planning department in particular need to formulate suitable strategies to combat the water scarcity scenario waiting ahead. The study suggests raising the use of surface water from nearby rivers to lessen the pressure on groundwater resources.

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Spatial Landslide Susceptibility Assessment Based on Novel Neural-Metaheuristic Geographic Information System Based Ensembles

Sensors ◽

10.3390/s19214698 ◽

2019 ◽

Vol 19 (21) ◽

pp. 4698 ◽

Cited By ~ 9

Author(s):

Hossein Moayedi ◽

Abdolreza Osouli ◽

Dieu Tien Bui ◽

Loke Kok Foong

Keyword(s):

Neural Network ◽

Spatial Interaction ◽

Characteristic Curve ◽

Absolute Error ◽

Optimization Techniques ◽

Slope Aspect ◽

Stream Power ◽

Topographic Wetness Index ◽

Learning Capability ◽

Conditioning Factors

Regular optimization techniques have been widely used in landslide-related problems. This paper outlines two novel optimizations of artificial neural network (ANN) using grey wolf optimization (GWO) and biogeography-based optimization (BBO) metaheuristic algorithms in the Ardabil province, Iran. To this end, these algorithms are synthesized with a multi-layer perceptron (MLP) neural network for optimizing its computational parameters. The used spatial database consists of fourteen landslide conditioning factors, namely elevation, slope aspect, land use, plan curvature, profile curvature, soil type, distance to river, distance to road, distance to fault, rainfall, slope degree, stream power index (SPI), topographic wetness index (TWI) and lithology. 70% of the identified landslides are randomly selected to train the proposed models and the remaining 30% is used to evaluate the accuracy of them. Also, the frequency ratio theory is used to analyze the spatial interaction between the landslide and conditioning factors. Obtained values of area under the receiver operating characteristic curve, as well as mean square error and mean absolute error showed that both GWO and BBO hybrid algorithms could efficiently improve the learning capability of the MLP. Besides, the BBO-based ensemble surpasses other implemented models.

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Integrated Geomorphological, Geospatial and AHP Technique for Groundwater Prospects Mapping in Basaltic Terrain

10.21523/gcj3.18020102 ◽

2018 ◽

Vol 2 (1) ◽

pp. 16-27 ◽

Cited By ~ 11

Author(s):

Vaishnavi Mundalik ◽

Clinton Fernandes ◽

Ajaykumar Kadam ◽

Bhavana Umrikar

Keyword(s):

Groundwater Resources ◽

Drainage Density ◽

Groundwater Potential ◽

The Sustainable Development ◽

Thematic Layers ◽

Groundwater Availability ◽

Basaltic Terrain ◽

Thematic Layer ◽

Increasing Demand ◽

Gis Environment

Groundwater is an important source of drinking water in rural parts of India. Because of the increasing demand for water, it is essential to identify new sources for the sustainable development of this resource. The potential mapping and exploration of groundwater resources have become a breakthrough in the field of hydrogeological research. In the present paper, a groundwater prospects map is delineated for the assessment of groundwater availability in Kar basin on basaltic terrain, using remote sensing and Geographic Information System (GIS) techniques. Various thematic layers such as geology, slope, soil, geomorphology, drainage density and rainfall are prepared using satellite data, topographic maps and field data. The ranks and weights were assigned to each thematic layer and various categories of those thematic layers using AHP technique respectively. Further, a weighted overlay analysis was performed by reclassifying them in the GIS environment to prepare the groundwater potential map of the study area. The results show that groundwater prospects map classified into three classes low, moderate and high having area 17.12%, 38.26%, 44.62%, respectively. The overlay map with the groundwater potential zones in the study area has been found to be helpful for better planning and managing the resources.

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Identification of Groundwater Potential Zones Using GIS and Multi-Criteria Decision-Making Techniques: A Case Study Upper Coruh River Basin (NE Turkey)

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10060396 ◽

2021 ◽

Vol 10 (6) ◽

pp. 396

Author(s):

Ümit Yıldırım

Keyword(s):

Drainage Density ◽

Groundwater Potential ◽

High Potential ◽

Groundwater Potential Zones ◽

Topographic Wetness Index ◽

Analytic Hierarchy ◽

Soil Thickness ◽

Çoruh River Basin ◽

Basin Area ◽

Very High

In this study, geographic information system (GIS)-based, analytic hierarchy process (AHP) techniques were used to identify groundwater potential zones to provide insight to decisionmakers and local authorities for present and future planning. Ten different geo-environmental factors, such as slope, topographic wetness index, geomorphology, drainage density, lithology, lineament density, rainfall, soil type, soil thickness, and land-use classes were selected as the decision criteria, and related GIS tools were used for creating, analysing and standardising the layers. The final groundwater potential zones map was delineated, using the weighted linear combination (WLC) aggregation method. The map was spatially classified into very high potential, high potential, moderate potential, low potential, and very low potential. The results showed that 21.5% of the basin area is characterised by high to very high groundwater potential. In comparison, the very low to low groundwater potential occupies 57.15%, and the moderate groundwater potential covers 21.4% of the basin area. Finally, the GWPZs map was investigated to validate the model, using discharges and depth to groundwater data related to 22 wells scattered over the basin. The validation results showed that GWPZs classes strongly overlap with the well discharges and groundwater depth located in the given area.

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